tilupy (ThIn-Layer Unified Processing in pYthon) package is meant as a top-level tool for processing inputs and outputs of thin-layer geophysical
flow simulations on general topographies.
It contains one submodule per thin-layer model for writing and reading raw inputs and outputs of the model.
Outputs are then easily compared between different simulations / models. The models themselves are not part of this package and must
be installed separately.
Note that tilupy is still under development, thus only minimal documentation is available at the moment, and testing is underway.
Contributions are feedback are most welcome. Reading and writing is available for the SHALTOP model (most commonly used by the author) and r.avaflow
(only partly maintained).
To install tilupy from GitHub or PyPi, you'll need to have pip installed on your computer.
It is strongly recommended to install tilupy in a virtual environnement dedicated to this package. This can be done with virtualenv
(see the documentation e.g. here).
Create the environnement with :
python -m venv /path/to/myenvand activate it, on Linux:
source /path/to/myenv/bin/activateand on Windows:
\path\to\myenv\Scripts\activate
Alternatively, if you are more used to Anaconda :
conda create -n tilupy pip
conda activate tilupyor equivalently with Mamba :
mamba create -n tilupy pip
mamba activate tilupyBefore installing with pip, make sure pip, steuptools and wheel are up to date
python -m pip install --upgrade pip setuptools wheel
python -m pip install tilupy
Download the GithHub repository here, or clone it with
git clone https://github.com/marcperuz/tilupy.git
Open a terminal in the created folder and type:
python -m pip install .
The latest stable version of tilupy on PyPi is also (supposedly) distributed on conda-forge. It can be intalled with Anaconda (or any equivalent) with
conda install conda-forge::tilupy
We give here a simple example to prepare simulations for SHALTOP, and process the results. The corresponding scripts can be found in examples/frankslide
Import the different required modules :
import os
# Read an write rasters
import tilupy.raster
# Functions to download examples of elevation and initial mass rasters
import tilupy.download_data
#Submodule used to prepare Shaltop simulations
import tilupy.models.shaltop.initsimus as shinitDefine the folder where input data will be downloaded and simulations carried out :
FOLDER_BASE = '/path/to/myfolder'Import data from GitHub, and create subfolder for simulation results
folder_data = os.path.join(FOLDER_BASE, 'rasters')
os.makedirs(folder_data, exist_ok=True)
#raster_topo and raster_mass are the paths to the topography and initial mass rasters
raster_topo = tilupy.download_data.import_frankslide_dem(folder_out=folder_data)
raster_mass = tilupy.download_data.import_frankslide_pile(folder_out=folder_data)
# Create folder for shaltop simulations
folder_simus = os.path.join(FOLDER_BASE, 'shaltop')
os.makedirs(folder_simus, exist_ok=True)Convert downloaded rasters to Shaltop input file type, and store the properties of the resulting grid
shinit.raster_to_shaltop_txtfile(raster_topo,
os.path.join(folder_simus, 'topography.d'))
axes_props = shinit.raster_to_shaltop_txtfile(raster_mass,
os.path.join(folder_simus, 'init_mass.d'))Initiate simulations parameters. See the SHALTOP documentation for details.
params = dict(nx=axes_props['nx'], ny=axes_props['ny'],
per=axes_props['nx']*axes_props['dx'],
pery=axes_props['ny']*axes_props['dy'],
tmax=100, # Simulation maximum time in seconds (not comutation time)
dt_im=10, # Time interval (s) between snapshots recordings
file_z_init = 'topography.d', # Name of topography input file
file_m_init = 'init_mass.d',# name of init mass input file
initz=0, # Topography is read from file
ipr=0, # Initial mass is read from file
hinit_vert=1, # Initial is given as vertical thicknesses and
# must be converted to thicknesses normal to topography
eps0=1e-13, #Minimum value for thicknesses and velocities
icomp=1, # choice of rheology (Coulomb with constant basal friction)
x0=1000, # Min x value (used for plots after simulation is over)
y0=2000) # Min y value (used for plots after simulation is over)Finally, prepare simulations for a set of given rheological parameters (here, three basal friction coefficients)
deltas = [15, 20, 25]
for delta in deltas:
params_txt = 'delta_{:05.2f}'.format(delta).replace('.', 'p')
params['folder_output'] = params_txt # Specify folder where outputs are stored
params['delta1'] = delta # Specify the friction coefficient
#Write parameter file
shinit.write_params_file(params, directory=folder_simus,
file_name=params_txt + '.txt')
#Create folder for results (not done by shlatop!)
os.makedirs(os.path.join(folder_simus, params_txt), exist_ok=True)You must then run the simulations (see Shaltop documentation)
Simulation results are read from a Results class. Main functions are defined in the parent class tilupy.read.Results, and each model has its own inheretied class tilupy.models.[model_name].read.Results. A class instance for a given model can be initiated with
res = tilupy.read.get_results([model_name], **kwargs)where `kwargs must be adapted to the considered model. For instance with Shaltop and the example above, the results of the simulation with a friction angle of 25 must be initiated as :
res = tilupy.read.get_results('shaltop', folder_base=folder_simus, file_params='delta_25p00.txt)The topography and axes can then directly be read from res :
x, y, z = res.x, res.y, res.z
import matplotlib.pyplot as plt
plt.imshow(z)z[i, j] is the altitude at flip(y[i]) and x[j]. Thus z[0, 0] corresponds to the North-West corner of the topography.
Specific simulation outputs can be extracted with
h_res = res.get_output(res_name)where res_name must chosen among
h: Flow thickness in the direction perpendicular to the topographyhvert: Flow thickness in the vertical directionuxanduy: Flow velocity in the X and Y direction (check whether it is in the cartesian reference frame or not)u: Norm of the flow velocityhu: Momentum (thickness * flow velocity)hu2: Kinetic energy (thickness * square flow velocity)
It is also possible to extract 2D spatial static characteritics of the flow by using any of the previous states with _[operation], where
[operation] is chosen among, max, mean, std, sum, min, final, initial. For instance h_max is a 2D array with the
maximum simulated thickness at each point of the grid. h_final is the simulated thickness at the end of the simulation.
tilupy will load these characteristics directly from the simulation output when possible (e.g. Shaltop records a maximum thickness array),
or compute then from the available data. For instance, if the simulation results contains a file whith the maximum thickness, h_max will be
read from this file. Otherwise, h_max is computed from the simulation recorded temporal snapshots (which is supposedly less precise).
For instance, to load the recorded thicknesses in the current example :
res_name = 'h'
h_res = res.get_output(res_name) # Thicknesses recorded at different times
# h_res.d is a 3D numpy array of dimension (len(x) x len(y) x len(h_res.t))
plt.imshow(h_res.d[:, :, -1]) # Plot final thickness.
t = h_res.t # Get times of simulation outputs.And to load the maximum thickness array :
res_name = 'h_max'
h_max_res = res.get_output(res_name) #h_max_res is read directly from simulation
# results when possible, and is deduced from res.get_output('h') otherwise
# h_max_res.d is a 2D numpy array of dimension (len(x) x len(y))
plt.imshow(h_max_res.d)tilupycan be used to plot the results as images, where the flow state (thickness, velocity, ...)
is shown with a colorscale on the underlying topography. Plots can be thouroughly customized (documentation not available yet).
For instance, the following code plots the flow thickness at each recorded time step, with a constant colorscale vraying from 0.1 to
100 m. The topography is represented as a shaded relief with thin contour lines every 10 m, and bold contour_lines every 100 m. Plots are saved
in a folder created in folder_out, but not displayed in the terminal (if you work in a developping environnement such as spyder).
topo_kwargs = dict(contour_step=10, step_contour_bold=100) # give interval between thin and bold contour lines
params_files = 'delta_*.txt'
tilupy.cmd.plot_results('shaltop', 'h', params_files, folder_simus,
save=True, display_plot=False, figsize=(15/2.54, 15/2.54),
vmin=0.1, vmax=100, fmt='png',
topo_kwargs=topo_kwargs)The following code acts similarly, but plots the maximum thickness instead, and used a segmented colormap given by cmap_intervals.
tilupy.cmd.plot_results('shaltop', 'h_max', params_files, folder_simus,
save=True, display_plot=False, figsize=(15/2.54, 15/2.54),
cmap_intervals=[0.1, 5, 10, 25, 50, 100],
topo_kwargs=topo_kwargs)It is also possible to save outputs back to rasters. The following code save all recorded thicknesses snapshots as tif files in a new folder
created in folder_out.
tilupy.cmd.to_raster('shaltop', 'h_max', params_files,
folder_simus, fmt='tif')tilupy comes with command line scripts to allow for quick processing of results. They work similarly as the functions tilupy.cmd.plot_results and tilupy.cmd.to_raster,
although there are less options.
tilupy_plot will automatically plot and save results in a new folder plot located in the simulation output folder specified in the parameter file :
tilupy_plot [-h] [-n RES_NAME] [-p PARAM_FILES] [-f FOLDER] [--fmt FMT] [--vmin VMIN] [--vmax VMAX]
[--minval_abs MINVAL_ABS]
[model]
RES_NAME can be any of the strings listed in the previous section. For instance, to plot all thicknesses snaphsots from shaltop simulations in the current folder, type tilupy_plot shaltop -n h. If parameters files are located in another/folder, type, tilupy shaltop -n h -f another/folder. Similarly, to save thicknesses snapshots as ascii rasters, use tilupy_to_raster shaltop -n h --fmt asc.
tilupy_to_raster [-h] [-n RES_NAME] [-p PARAM_FILES] [-f FOLDER] [--fmt {tif,tiff,txt,asc,ascii}] model
With both commands you can use the -h option do print help.
We provide here a basic descriptions of models compatible with tilupy. The list of references is not exhaustive.
shaltop models gravitational flow models over general topographies with small slope variation (small curvature) and friction. The equations are expressed in a horizontal/vertical reference frame and the shallow approximation is imposed in the direction normal to the slope. shaltop is not yet freely available. If you are interested, contact m.peruzzetto@brgm.fr or mangeney@ipgp.fr.
- Bouchut, F., Mangeney-Castelnau, A., Perthame, B., Vilotte, J.-P., 2003. A new model of Saint Venant and Savage–Hutter type for gravity driven shallow water flows. Comptes Rendus Mathématique 336, 531–536. https://doi.org/10.1016/S1631-073X(03)00117-1
- Bouchut, F., Westdickenberg, M., 2004. Gravity driven shallow water models for arbitrary topography. Communications in Mathematical Sciences 2, 359–389. https://doi.org/10.4310/CMS.2004.v2.n3.a2
- Mangeney-Castelnau, A., Bouchut, F., Vilotte, J.P., Lajeunesse, E., Aubertin, A., Pirulli, M., 2005. On the use of Saint Venant equations to simulate the spreading of a granular mass: numerical simulation of granular spreading. Journal of Geophysical Research: Solid Earth 110, B09103. https://doi.org/10.1029/2004JB003161
- Mangeney, A., Bouchut, F., Thomas, N., Vilotte, J.P., Bristeau, M.O., 2007. Numerical modeling of self-channeling granular flows and of their levee-channel deposits. Journal of Geophysical Research 112, F02017. https://doi.org/10.1029/2006JF000469
r.avaflow is a GIS-supported open source software tool for the simulation of complex, cascading mass flows over arbitrary topography. It can be downloaded, along with the associated documentation, on the officiel website. Note that the integration of r.avaflow in tilupy is partial and potentially not adapted to new releases of r.avaflow.
- Mergili, M., Fischer, J.-T., Krenn, J., Pudasaini, S.P., 2017. r.avaflow v1, an advanced open source computational framework for the propagation and interaction of two-phase mass flows. Geoscientific Model Development Discussions 10, 553–569. https://doi.org/10.5194/gmd-10-553-2017
- Pudasaini, S.P., Mergili, M., 2019. A Multi-Phase Mass Flow Model. Journal of Geophysical Research: Earth Surface 124, 2920–2942. https://doi.org/10.1029/2019JF005204
Lave2D is a software developped by the INRAE for the modeling of debris flows with the Herschel-Bulkley rheology.
- Laigle, D., Hector, A.-F., Hübl, J., Rickenmann, D., 2006. Confrontation de la simulation numérique de l’étalement de laves torrentielles boueuses à des observations d’événements réels. La Houille Blanche 92, 105–112. https://doi.org/10.1051/lhb:2006108
- Rickenmann, D., Laigle, D., McArdell, B.W., Hübl, J., 2006. Comparison of 2D debris-flow simulation models with field events. Computational Geosciences 10, 241–264. https://doi.org/10.1007/s10596-005-9021-3